What is Synthetic Data?

What is Synthetic Data?

Synthetic data is artificially generated data that mimics the statistical properties and structure of real-world data. Instead of collecting data from actual events, synthetic data is created by algorithms, simulations, or generative models to be used for training, testing, and validating AI systems.

Why It Matters

Real-world data is often scarce, expensive, biased, or privacy-restricted. Synthetic data solves all four problems: it can be generated in unlimited quantities, at low cost, with controlled distributions, and without exposing personal information. It's increasingly critical for training autonomous vehicles (simulated driving scenarios), medical AI (synthetic patient records), and for augmenting underrepresented classes in training datasets.

How It Works

Synthetic data generation approaches:

1. Rule-based generation:

• Define statistical distributions and business rules
• Generate data points that match those distributions
• Simple but limited in capturing real-world complexity

2. Generative model-based:

• Train a GAN, VAE, or diffusion model on real data
• The generator produces new samples that follow the learned distribution
• Can produce realistic tabular data, images, text, and time series

3. Simulation-based:

• Build a physics or business simulation of the real world
• Generate data from simulated scenarios
• Common for autonomous driving (CARLA, AirSim), robotics, and game AI

4. LLM-based:

• Use large language models to generate training examples
• Prompt the LLM to produce diverse text samples for a given task
• Self-instruct and Alpaca-style dataset generation

Key considerations:

• Fidelity — how closely does the synthetic data match real data distributions?
• Privacy — can real individuals be re-identified from synthetic data? (differential privacy helps)
• Diversity — does the synthetic data cover edge cases and underrepresented scenarios?
• Validation — synthetic models must be validated against held-out real data

Example

A bank needs to train a fraud detection model but has very few fraud examples (0.1% of transactions). Using a generative model trained on the few real fraud cases, they create 10,000 synthetic fraud transactions that preserve the statistical patterns of real fraud — giving the model enough examples to learn from without compromising customer data.